A pattern for running long, raw, dictated work orders through AI coding agents.
One brief goes in. It gets cut into work packages. Each package passes a build station and two independent inspection stations. Nothing leaves the line uninspected.
BRIEF ──► [cut] ──► [build] ──► [inspect] ──┐
▲ │
Steps to deploy a Node.js app to DigitalOcean using PM2, NGINX as a reverse proxy and an SSL from LetsEncrypt
Create free AWS Account at https://aws.amazon.com/
I would be creating a t2.medium ubuntu machine for this demo.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| var GB2260 = { | |
| "110000": "北京市", | |
| "110100": "北京市市辖区", | |
| "110101": "北京市东城区", | |
| "110102": "北京市西城区", | |
| "110103": "北京市崇文区", | |
| "110104": "北京市宣武区", | |
| "110105": "北京市朝阳区", | |
| "110106": "北京市丰台区", | |
| "110107": "北京市石景山区", |
| # Please Note, I am sharing this, hoping this will be used for good public use, such as data analysis, penetration testing etc | |
| # These links are already available all over the internet | |
| # Also Note, Trying to login into someone else's account without their permission is unethical and illegal | |
| # Collection 1 | |
| magnet:?xt=urn:btih:B39C603C7E18DB8262067C5926E7D5EA5D20E12E&dn=Collection%201&tr=udp%3a%2f%2ftracker.coppersurfer.tk%3a6969%2fannounce&tr=udp%3a%2f%2ftracker.leechers-paradise.org%3a6969%2fannounce&tr=http%3a%2f%2ft.nyaatracker.com%3a80%2fannounce&tr=http%3a%2f%2fopentracker.xyz%3a80%2fannounce | |
| # Collection 2-5 & Antipublic | |
| magnet:?xt=urn:btih:D136B1ADDE531F38311FBF43FB96FC26DF1A34CD&dn=Collection%20%232-%235%20%26%20Antipublic&tr=udp%3a%2f%2ftracker.coppersurfer.tk%3a6969%2fannounce&tr=udp%3a%2f%2ftracker.leechers-paradise.org%3a6969%2fannounce&tr=http%3a%2f%2ft.nyaatracker.com%3a80%2fannounce&tr=http%3a%2f%2fopentracker.xyz%3a80%2fannounce |
git init --bare $HOME/.dotfilesalias dot='/usr/bin/git --git-dir=$HOME/.dotfiles/ --work-tree=$HOME'git e.g. dot to configure the .dotfiles repository.dotfiles config --local status.showUntrackedFiles nogit status.
Export all your ChatGPT conversations as JSON + Markdown + HTML + ZIP. Works with ChatGPT Business/Team/Enterprise accounts (including SSO/Okta).
| -- Copy file path / selection reference for pasting into AI chats | |
| local function copy_ref(opts) | |
| -- "%" is the current buffer's file name; ":." makes it relative to the cwd | |
| local path = vim.fn.expand("%:.") | |
| -- ref is what ends up in the clipboard; start with just the path | |
| local ref = path | |
| if opts.visual then | |
| -- '< and '> are only set after leaving visual mode, so read the live selection: | |
| -- "v" is the line where visual mode was started (the anchor) |